Applied Statistical Decision Theory / Edition 1

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Overview

"In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems."
—From the foreword to their classic work Applied Statistical Decision Theory. First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley

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Editorial Reviews

Booknews
Addressed to those who are interested in using statistics as a tool in practical problems of decision making under conditions of uncertainty and who also have the necessary training in mathematics and statistics to employ these analytical techniques. The underlying principle of the material outlined in the book is the Bayesian approach which provides a formal mechanism for taking account of preferences and weights rather than leaving it up to the decision maker's intuition. Topics covered include sufficient statistics and noninformative stopping, conjugate prior distributions, additive utility and opportunity loss, linear terminal analysis, selection of processes, problems in which the act and state spaces coincide, multivariate normalized density functions, Bernoulli process, Poisson process, independent normal process, and normal regression process. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780471383499
  • Publisher: Wiley
  • Publication date: 6/2/2000
  • Series: Wiley Classics Library Series , #78
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 356
  • Product dimensions: 6.08 (w) x 8.96 (h) x 0.92 (d)

Meet the Author

Howard Raiffa is Frank P. Ramsey Professor of Managerial Economics (Emeritus), Harvard Business School and Harvard's Kennedy School of Government.

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Table of Contents

EXPERIMENTATION AND DECISION: GENERAL THEORY.

The Problem and the Two Basic Modes of Analysis.

Sufficient Statistics and Noninformative Stopping.

Conjugate Prior Distributions.

EXTENSIVE-FORM ANALYSIS WHEN SAMPLING AND TERMINAL UTILITIES ARE ADDITIVE.

Additive Utility, Opportunity Loss, and the Value of Information: Introduction to Part II.

Linear Terminal Analysis.

Selection of the Best of Several Processes.

Problems in Which the Act and State Spaces Coincide.

DISTRIBUTION THEORY.

Univariate Normalized Mass and Density Functions.

Multivariate Normalized Density Functions.

Bernoulli Process.

Poisson Process.

Independent Normal Process.

Independent Multinormal Process.

Normal Regression Process.

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